Overview

Dataset statistics

Number of variables27
Number of observations203
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.9 KiB
Average record size in memory216.7 B

Variable types

Numeric16
Categorical11

Alerts

modele has a high cardinality: 140 distinct valuesHigh cardinality
car_ID is highly overall correlated with marqueHigh correlation
niveau_risque_assurance is highly overall correlated with empattement(cm) and 2 other fieldsHigh correlation
empattement(cm) is highly overall correlated with niveau_risque_assurance and 11 other fieldsHigh correlation
longueur_voiture(cm) is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
largeur_voiture(cm) is highly overall correlated with empattement(cm) and 10 other fieldsHigh correlation
hauteur_voiture(cm) is highly overall correlated with niveau_risque_assurance and 3 other fieldsHigh correlation
poids_vehicule(kg) is highly overall correlated with empattement(cm) and 8 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement(cm) and 12 other fieldsHigh correlation
taux_alésage(cm) is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
course_piston(cm) is highly overall correlated with emplacement_moteur and 1 other fieldsHigh correlation
taux_compression is highly overall correlated with carburant and 3 other fieldsHigh correlation
chevaux is highly overall correlated with empattement(cm) and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville(L/100km) is highly overall correlated with longueur_voiture(cm) and 7 other fieldsHigh correlation
consommation_autoroute(L/100km) is highly overall correlated with empattement(cm) and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement(cm) and 8 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with niveau_risque_assurance and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
roues_motrices is highly overall correlated with marqueHigh correlation
emplacement_moteur is highly overall correlated with empattement(cm) and 4 other fieldsHigh correlation
type_moteur is highly overall correlated with taille_moteur and 3 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with largeur_voiture(cm) and 6 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 3 other fieldsHigh correlation
marque is highly overall correlated with car_ID and 10 other fieldsHigh correlation
carburant is highly imbalanced (53.6%)Imbalance
emplacement_moteur is highly imbalanced (88.9%)Imbalance
nombre_cylindres is highly imbalanced (57.3%)Imbalance
car_ID is uniformly distributedUniform
modele is uniformly distributedUniform
car_ID has unique valuesUnique
niveau_risque_assurance has 66 (32.5%) zerosZeros

Reproduction

Analysis started2023-04-26 08:18:43.444612
Analysis finished2023-04-26 08:19:19.360707
Duration35.92 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

car_ID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct203
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.63054
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:19.513635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.1
Q151.5
median102
Q3154.5
95-th percentile194.9
Maximum205
Range204
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.497287
Coefficient of variation (CV)0.57972302
Kurtosis-1.204884
Mean102.63054
Median Absolute Deviation (MAD)52
Skewness0.016298887
Sum20834
Variance3539.9272
MonotonicityStrictly increasing
2023-04-26T10:19:19.686662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
141 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
Other values (193) 193
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%

niveau_risque_assurance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83251232
Minimum-2
Maximum3
Zeros66
Zeros (%)32.5%
Negative25
Negative (%)12.3%
Memory size1.7 KiB
2023-04-26T10:19:19.833420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2473842
Coefficient of variation (CV)1.4983373
Kurtosis-0.67225171
Mean0.83251232
Median Absolute Deviation (MAD)1
Skewness0.2135015
Sum169
Variance1.5559674
MonotonicityNot monotonic
2023-04-26T10:19:19.942907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
-1 22
 
10.8%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.8%
0 66
32.5%
1 54
26.6%
2 31
15.3%
3 27
13.3%
ValueCountFrequency (%)
3 27
13.3%
2 31
15.3%
1 54
26.6%
0 66
32.5%
-1 22
 
10.8%
-2 3
 
1.5%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
183 
diesel
20 

Length

Max length6
Median length3
Mean length3.2955665
Min length3

Characters and Unicode

Total characters669
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 183
90.1%
diesel 20
 
9.9%

Length

2023-04-26T10:19:20.070126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:20.223273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
gas 183
90.1%
diesel 20
 
9.9%

Most occurring characters

ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 669
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 669
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 203
30.3%
g 183
27.4%
a 183
27.4%
e 40
 
6.0%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
166 
turbo
37 

Length

Max length5
Median length3
Mean length3.364532
Min length3

Characters and Unicode

Total characters683
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 166
81.8%
turbo 37
 
18.2%

Length

2023-04-26T10:19:20.346055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:20.512635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
std 166
81.8%
turbo 37
 
18.2%

Most occurring characters

ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 683
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 683
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 203
29.7%
s 166
24.3%
d 166
24.3%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
114 
two
89 

Length

Max length4
Median length4
Mean length3.5615764
Min length3

Characters and Unicode

Total characters723
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 114
56.2%
two 89
43.8%

Length

2023-04-26T10:19:20.624264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:20.764742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
four 114
56.2%
two 89
43.8%

Most occurring characters

ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 723
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 723
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 203
28.1%
f 114
15.8%
u 114
15.8%
r 114
15.8%
t 89
12.3%
w 89
12.3%

type_vehicule
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
berline
95 
hayon
69 
break
25 
coupé
 
8
décapotable
 
6

Length

Max length11
Median length5
Mean length6.1133005
Min length5

Characters and Unicode

Total characters1241
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdécapotable
2nd rowdécapotable
3rd rowhayon
4th rowberline
5th rowberline

Common Values

ValueCountFrequency (%)
berline 95
46.8%
hayon 69
34.0%
break 25
 
12.3%
coupé 8
 
3.9%
décapotable 6
 
3.0%

Length

2023-04-26T10:19:20.876325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:21.029910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
berline 95
46.8%
hayon 69
34.0%
break 25
 
12.3%
coupé 8
 
3.9%
décapotable 6
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e 221
17.8%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
y 69
 
5.6%
h 69
 
5.6%
Other values (7) 87
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1241
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 221
17.8%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
y 69
 
5.6%
h 69
 
5.6%
Other values (7) 87
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1241
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 221
17.8%
n 164
13.2%
b 126
10.2%
r 120
9.7%
a 106
8.5%
l 101
8.1%
i 95
7.7%
o 83
 
6.7%
y 69
 
5.6%
h 69
 
5.6%
Other values (7) 87
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1227
98.9%
None 14
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 221
18.0%
n 164
13.4%
b 126
10.3%
r 120
9.8%
a 106
8.6%
l 101
8.2%
i 95
7.7%
o 83
 
6.8%
y 69
 
5.6%
h 69
 
5.6%
Other values (6) 73
 
5.9%
None
ValueCountFrequency (%)
é 14
100.0%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
traction
118 
propulsion
76 
quatre_roues_motrices
 
9

Length

Max length21
Median length8
Mean length9.3251232
Min length8

Characters and Unicode

Total characters1893
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpropulsion
2nd rowpropulsion
3rd rowpropulsion
4th rowtraction
5th rowquatre_roues_motrices

Common Values

ValueCountFrequency (%)
traction 118
58.1%
propulsion 76
37.4%
quatre_roues_motrices 9
 
4.4%

Length

2023-04-26T10:19:21.161124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:21.300002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
traction 118
58.1%
propulsion 76
37.4%
quatre_roues_motrices 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
o 288
15.2%
t 254
13.4%
r 221
11.7%
i 203
10.7%
n 194
10.2%
p 152
8.0%
a 127
6.7%
c 127
6.7%
u 94
 
5.0%
s 94
 
5.0%
Other values (5) 139
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1875
99.0%
Connector Punctuation 18
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 288
15.4%
t 254
13.5%
r 221
11.8%
i 203
10.8%
n 194
10.3%
p 152
8.1%
a 127
6.8%
c 127
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.5%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1875
99.0%
Common 18
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 288
15.4%
t 254
13.5%
r 221
11.8%
i 203
10.8%
n 194
10.3%
p 152
8.1%
a 127
6.8%
c 127
6.8%
u 94
 
5.0%
s 94
 
5.0%
Other values (4) 121
6.5%
Common
ValueCountFrequency (%)
_ 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 288
15.2%
t 254
13.4%
r 221
11.7%
i 203
10.7%
n 194
10.2%
p 152
8.0%
a 127
6.7%
c 127
6.7%
u 94
 
5.0%
s 94
 
5.0%
Other values (5) 139
7.3%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
200 
rear
 
3

Length

Max length5
Median length5
Mean length4.9852217
Min length4

Characters and Unicode

Total characters1012
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 200
98.5%
rear 3
 
1.5%

Length

2023-04-26T10:19:21.418742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:21.550702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
front 200
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 206
20.4%
f 200
19.8%
o 200
19.8%
n 200
19.8%
t 200
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1012
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 206
20.4%
f 200
19.8%
o 200
19.8%
n 200
19.8%
t 200
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1012
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 206
20.4%
f 200
19.8%
o 200
19.8%
n 200
19.8%
t 200
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 206
20.4%
f 200
19.8%
o 200
19.8%
n 200
19.8%
t 200
19.8%
e 3
 
0.3%
a 3
 
0.3%

empattement(cm)
Real number (ℝ)

Distinct53
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.92118
Minimum220
Maximum307.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:21.685031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile236.23
Q1240
median246.4
Q3260.1
95-th percentile279.4
Maximum307.1
Range87.1
Interquartile range (IQR)20.1

Descriptive statistics

Standard deviation15.343112
Coefficient of variation (CV)0.061147137
Kurtosis0.98481743
Mean250.92118
Median Absolute Deviation (MAD)6.9
Skewness1.0382006
Sum50937
Variance235.41108
MonotonicityNot monotonic
2023-04-26T10:19:21.841294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 21
 
10.3%
238 19
 
9.4%
243.1 13
 
6.4%
245.1 8
 
3.9%
249.9 7
 
3.4%
247.1 7
 
3.4%
251 6
 
3.0%
244.6 6
 
3.0%
251.7 6
 
3.0%
274.1 6
 
3.0%
Other values (43) 104
51.2%
ValueCountFrequency (%)
220 2
 
1.0%
224.5 1
 
0.5%
225 2
 
1.0%
227.3 3
 
1.5%
231.9 2
 
1.0%
236.2 1
 
0.5%
236.5 5
 
2.5%
237 1
 
0.5%
238 19
9.4%
239.5 1
 
0.5%
ValueCountFrequency (%)
307.1 1
 
0.5%
293.6 2
 
1.0%
290.1 4
2.0%
287 2
 
1.0%
284.5 1
 
0.5%
279.4 3
1.5%
277.1 5
2.5%
274.3 1
 
0.5%
274.1 6
3.0%
271 1
 
0.5%

longueur_voiture(cm)
Real number (ℝ)

Distinct74
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.32463
Minimum358.4
Maximum528.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:21.998740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum358.4
5-th percentile399.5
Q1423.05
median439.9
Q3465.6
95-th percentile499.59
Maximum528.6
Range170.2
Interquartile range (IQR)42.55

Descriptive statistics

Standard deviation31.347852
Coefficient of variation (CV)0.07087069
Kurtosis-0.079096131
Mean442.32463
Median Absolute Deviation (MAD)17.5
Skewness0.14808281
Sum89791.9
Variance982.68781
MonotonicityNot monotonic
2023-04-26T10:19:22.192904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399.5 15
 
7.4%
479.6 11
 
5.4%
436.1 7
 
3.4%
474.2 7
 
3.4%
422.4 7
 
3.4%
419.9 6
 
3.0%
451.6 6
 
3.0%
447.5 6
 
3.0%
474 6
 
3.0%
449.1 5
 
2.5%
Other values (64) 127
62.6%
ValueCountFrequency (%)
358.4 1
 
0.5%
367.3 2
 
1.0%
381 3
 
1.5%
396 3
 
1.5%
399 1
 
0.5%
399.5 15
7.4%
401.1 1
 
0.5%
403.1 3
 
1.5%
403.4 1
 
0.5%
404.1 3
 
1.5%
ValueCountFrequency (%)
528.6 1
 
0.5%
514.6 2
1.0%
507 2
1.0%
506 1
 
0.5%
505.2 4
2.0%
500.4 1
 
0.5%
492.3 1
 
0.5%
489.5 3
1.5%
486.9 1
 
0.5%
484.9 2
1.0%

largeur_voiture(cm)
Real number (ℝ)

Distinct43
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.45025
Minimum153.2
Maximum183.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:22.357087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum153.2
5-th percentile161.5
Q1162.8
median166.4
Q3169.9
95-th percentile179.05
Maximum183.6
Range30.4
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation5.4506006
Coefficient of variation (CV)0.032550568
Kurtosis0.69436052
Mean167.45025
Median Absolute Deviation (MAD)3.5
Skewness0.90163952
Sum33992.4
Variance29.709047
MonotonicityNot monotonic
2023-04-26T10:19:22.501793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
162.1 24
 
11.8%
168.9 23
 
11.3%
166.1 14
 
6.9%
161.5 11
 
5.4%
173.7 10
 
4.9%
163.6 10
 
4.9%
162.6 9
 
4.4%
166.4 8
 
3.9%
165.6 7
 
3.4%
166.6 6
 
3.0%
Other values (33) 81
39.9%
ValueCountFrequency (%)
153.2 1
 
0.5%
157 1
 
0.5%
158.8 1
 
0.5%
161.5 11
5.4%
162.1 24
11.8%
162.3 3
 
1.5%
162.6 9
 
4.4%
162.8 2
 
1.0%
163.1 6
 
3.0%
163.6 10
4.9%
ValueCountFrequency (%)
183.6 1
 
0.5%
182.9 1
 
0.5%
182.1 3
1.5%
181.4 3
1.5%
180.1 1
 
0.5%
179.3 1
 
0.5%
179.1 1
 
0.5%
178.6 3
1.5%
176.8 2
1.0%
175 4
2.0%

hauteur_voiture(cm)
Real number (ℝ)

Distinct49
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.47389
Minimum121.4
Maximum151.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:22.676407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum121.4
5-th percentile126.2
Q1132.1
median137.4
Q3141
95-th percentile146
Maximum151.9
Range30.5
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation6.2320283
Coefficient of variation (CV)0.045664619
Kurtosis-0.46343206
Mean136.47389
Median Absolute Deviation (MAD)4.1
Skewness0.05588914
Sum27704.2
Variance38.838176
MonotonicityNot monotonic
2023-04-26T10:19:22.839651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
129 14
 
6.9%
141.5 12
 
5.9%
132.1 12
 
5.9%
138.4 10
 
4.9%
137.4 10
 
4.9%
141 9
 
4.4%
137.9 8
 
3.9%
144 8
 
3.9%
142.5 7
 
3.4%
133.6 7
 
3.4%
Other values (39) 106
52.2%
ValueCountFrequency (%)
121.4 1
 
0.5%
124 2
 
1.0%
125.5 2
 
1.0%
126 4
 
2.0%
126.2 3
 
1.5%
127.5 6
3.0%
128.3 2
 
1.0%
128.5 5
 
2.5%
129 14
6.9%
129.5 1
 
0.5%
ValueCountFrequency (%)
151.9 2
 
1.0%
150.1 3
 
1.5%
149.1 4
2.0%
148.1 1
 
0.5%
146 3
 
1.5%
144 8
3.9%
143.5 2
 
1.0%
143 2
 
1.0%
142.7 3
 
1.5%
142.5 7
3.4%

poids_vehicule(kg)
Real number (ℝ)

Distinct170
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.2286
Minimum674.9
Maximum1844.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:23.003469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum674.9
5-th percentile862.03
Q1988.6
median1097.7
Q31335.15
95-th percentile1589.35
Maximum1844.3
Range1169.4
Interquartile range (IQR)346.55

Descriptive statistics

Standard deviation236.42286
Coefficient of variation (CV)0.20359718
Kurtosis-0.055777182
Mean1161.2286
Median Absolute Deviation (MAD)177.3
Skewness0.66680499
Sum235729.4
Variance55895.768
MonotonicityNot monotonic
2023-04-26T10:19:23.169070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081.8 4
 
2.0%
902.2 3
 
1.5%
870 3
 
1.5%
1031.9 3
 
1.5%
1155.8 2
 
1.0%
993.8 2
 
1.0%
1149.9 2
 
1.0%
918.1 2
 
1.0%
1095 2
 
1.0%
1844.3 2
 
1.0%
Other values (160) 178
87.7%
ValueCountFrequency (%)
674.9 1
0.5%
777 1
0.5%
825.1 1
0.5%
833.2 1
0.5%
850 2
1.0%
850.9 2
1.0%
856.8 1
0.5%
857.3 1
0.5%
861.8 1
0.5%
864.1 1
0.5%
ValueCountFrequency (%)
1844.3 2
1.0%
1791.7 1
0.5%
1769 1
0.5%
1710 1
0.5%
1701 1
0.5%
1696.4 1
0.5%
1685.1 1
0.5%
1671.5 1
0.5%
1594.4 1
0.5%
1589.8 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcv
 
13
ohcf
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1182266
Min length1

Characters and Unicode

Total characters633
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.9%
ohcv 13
 
6.4%
ohcf 13
 
6.4%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-04-26T10:19:23.322776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:23.495239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.9%
ohcv 13
 
6.4%
ohcf 13
 
6.4%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 633
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 633
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 195
30.8%
h 187
29.5%
c 187
29.5%
v 14
 
2.2%
f 13
 
2.1%
d 13
 
2.1%
l 12
 
1.9%
r 8
 
1.3%
t 4
 
0.6%

nombre_cylindres
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
157 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9014778
Min length3

Characters and Unicode

Total characters792
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 157
77.3%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-04-26T10:19:23.634387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:23.801624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
four 157
77.3%
six 24
 
11.8%
five 11
 
5.4%
eight 5
 
2.5%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 792
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 168
21.2%
o 161
20.3%
r 158
19.9%
u 157
19.8%
i 40
 
5.1%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.14778
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:23.954124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197.5
median120
Q3143
95-th percentile202.1
Maximum326
Range265
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation41.773527
Coefficient of variation (CV)0.3285431
Kurtosis5.2380141
Mean127.14778
Median Absolute Deviation (MAD)23
Skewness1.9335616
Sum25811
Variance1745.0276
MonotonicityNot monotonic
2023-04-26T10:19:24.109806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.4%
92 15
 
7.4%
98 14
 
6.9%
97 13
 
6.4%
90 12
 
5.9%
108 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
43.3%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.5%
92 15
7.4%
97 13
6.4%
98 14
6.9%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
3.0%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
64 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.8965517
Min length3

Characters and Unicode

Total characters791
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
46.3%
2bbl 64
31.5%
idi 20
 
9.9%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-04-26T10:19:24.251571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-26T10:19:24.414476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
46.3%
2bbl 64
31.5%
idi 20
 
9.9%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 156
19.7%
i 145
18.3%
p 104
13.1%
f 96
12.1%
m 95
12.0%
l 78
9.9%
2 64
8.1%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 713
90.1%
Decimal Number 78
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 156
21.9%
i 145
20.3%
p 104
14.6%
f 96
13.5%
m 95
13.3%
l 78
10.9%
d 29
 
4.1%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 713
90.1%
Common 78
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 156
21.9%
i 145
20.3%
p 104
14.6%
f 96
13.5%
m 95
13.3%
l 78
10.9%
d 29
 
4.1%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 64
82.1%
1 11
 
14.1%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 156
19.7%
i 145
18.3%
p 104
13.1%
f 96
12.1%
m 95
12.0%
l 78
9.9%
2 64
8.1%
d 29
 
3.7%
1 11
 
1.4%
s 10
 
1.3%

taux_alésage(cm)
Real number (ℝ)

Distinct25
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4477833
Minimum6.5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:24.562743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile7.5
Q18
median8.4
Q39.1
95-th percentile9.6
Maximum10
Range3.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.69377181
Coefficient of variation (CV)0.082124717
Kurtosis-0.80976417
Mean8.4477833
Median Absolute Deviation (MAD)0.6
Skewness0.037693008
Sum1714.9
Variance0.48131932
MonotonicityNot monotonic
2023-04-26T10:19:24.689839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
9.2 24
 
11.8%
8.1 23
 
11.3%
7.7 18
 
8.9%
8 17
 
8.4%
7.5 12
 
5.9%
8.8 11
 
5.4%
8.5 11
 
5.4%
9.1 10
 
4.9%
9.6 9
 
4.4%
8.7 8
 
3.9%
Other values (15) 60
29.6%
ValueCountFrequency (%)
6.5 1
 
0.5%
6.8 1
 
0.5%
7.4 8
 
3.9%
7.5 12
5.9%
7.6 6
 
3.0%
7.7 18
8.9%
7.8 1
 
0.5%
8 17
8.4%
8.1 23
11.3%
8.2 2
 
1.0%
ValueCountFrequency (%)
10 2
 
1.0%
9.7 2
 
1.0%
9.6 9
 
4.4%
9.5 3
 
1.5%
9.4 5
 
2.5%
9.2 24
11.8%
9.1 10
4.9%
9 6
 
3.0%
8.9 2
 
1.0%
8.8 11
5.4%

course_piston(cm)
Real number (ℝ)

Distinct27
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2857143
Minimum5.3
Maximum10.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:24.834710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile6.7
Q17.9
median8.4
Q38.7
95-th percentile9.2
Maximum10.6
Range5.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.77174282
Coefficient of variation (CV)0.093141375
Kurtosis2.3784789
Mean8.2857143
Median Absolute Deviation (MAD)0.4
Skewness-0.64595557
Sum1682
Variance0.59558699
MonotonicityNot monotonic
2023-04-26T10:19:24.961061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8.6 33
16.3%
8.2 15
 
7.4%
8 15
 
7.4%
7.7 14
 
6.9%
8.8 12
 
5.9%
8.9 11
 
5.4%
6.7 10
 
4.9%
8.3 10
 
4.9%
8.4 9
 
4.4%
8.5 9
 
4.4%
Other values (17) 65
32.0%
ValueCountFrequency (%)
5.3 1
 
0.5%
5.6 2
 
1.0%
6.7 10
4.9%
6.8 2
 
1.0%
7 1
 
0.5%
7.1 2
 
1.0%
7.3 1
 
0.5%
7.4 3
 
1.5%
7.7 14
6.9%
7.8 8
3.9%
ValueCountFrequency (%)
10.6 2
 
1.0%
9.9 3
 
1.5%
9.8 4
 
2.0%
9.2 5
 
2.5%
9.1 6
 
3.0%
9 4
 
2.0%
8.9 11
 
5.4%
8.8 12
 
5.9%
8.7 6
 
3.0%
8.6 33
16.3%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15133
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:25.092782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.55
median9
Q39.4
95-th percentile21.86
Maximum23
Range16
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation3.9905801
Coefficient of variation (CV)0.39310909
Kurtosis5.1392945
Mean10.15133
Median Absolute Deviation (MAD)0.4
Skewness2.5938477
Sum2060.72
Variance15.924729
MonotonicityNot monotonic
2023-04-26T10:19:25.222007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 45
22.2%
9.4 26
12.8%
8.5 14
 
6.9%
9.5 12
 
5.9%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.5%
Other values (22) 58
28.6%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.5%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.5%
8.5 14
6.9%
ValueCountFrequency (%)
23 5
2.5%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.5%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.39901
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:25.380550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile181.4
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.631013
Coefficient of variation (CV)0.37961099
Kurtosis2.6470236
Mean104.39901
Median Absolute Deviation (MAD)25
Skewness1.3928436
Sum21193
Variance1570.6172
MonotonicityNot monotonic
2023-04-26T10:19:25.543759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.4%
70 11
 
5.4%
116 9
 
4.4%
69 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
3.0%
160 6
 
3.0%
101 6
 
3.0%
62 6
 
3.0%
Other values (49) 116
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.0%
64 1
 
0.5%
68 19
9.4%
69 9
4.4%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5127.8325
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:25.676831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5990
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation478.5252
Coefficient of variation (CV)0.093319195
Kurtosis0.074796639
Mean5127.8325
Median Absolute Deviation (MAD)300
Skewness0.060106102
Sum1040950
Variance228986.37
MonotonicityNot monotonic
2023-04-26T10:19:25.804935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5500 37
18.2%
4800 35
17.2%
5000 27
13.3%
5200 23
11.3%
5400 13
 
6.4%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.5%
Other values (12) 33
16.3%
ValueCountFrequency (%)
4150 5
 
2.5%
4200 5
 
2.5%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 35
17.2%
5000 27
13.3%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.2%
5400 13
 
6.4%
5300 1
 
0.5%
5250 7
 
3.4%
Distinct28
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9831562
Minimum4.8003061
Maximum18.093462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:25.931217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.8003061
5-th percentile6.3571622
Q17.8405
median9.800625
Q312.379737
95-th percentile14.700938
Maximum18.093462
Range13.293155
Interquartile range (IQR)4.5392368

Descriptive statistics

Standard deviation2.5761492
Coefficient of variation (CV)0.25804958
Kurtosis-0.17496577
Mean9.9831562
Median Absolute Deviation (MAD)2.2130444
Skewness0.5511603
Sum2026.5807
Variance6.6365448
MonotonicityNot monotonic
2023-04-26T10:19:26.077640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7.587580645 27
13.3%
12.37973684 27
13.3%
9.800625 22
10.8%
8.711666667 14
 
6.9%
13.83617647 13
 
6.4%
9.046730769 12
 
5.9%
10.22673913 12
 
5.9%
11.20071429 8
 
3.9%
9.4086 8
 
3.9%
7.8405 8
 
3.9%
Other values (18) 52
25.6%
ValueCountFrequency (%)
4.800306122 1
 
0.5%
5.004574468 1
 
0.5%
5.227 1
 
0.5%
6.189868421 7
 
3.4%
6.357162162 6
 
3.0%
6.53375 1
 
0.5%
6.720428571 1
 
0.5%
6.918088235 1
 
0.5%
7.127727273 1
 
0.5%
7.587580645 27
13.3%
ValueCountFrequency (%)
18.09346154 1
 
0.5%
16.80107143 2
 
1.0%
15.681 3
 
1.5%
14.7009375 6
 
3.0%
13.83617647 13
6.4%
13.0675 3
 
1.5%
12.37973684 27
13.3%
11.76075 3
 
1.5%
11.20071429 8
 
3.9%
10.69159091 4
 
2.0%
Distinct30
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0574246
Minimum4.3558333
Maximum14.700938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:26.225363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4.3558333
5-th percentile5.4831404
Q16.9180882
median7.8405
Q39.4086
95-th percentile10.691591
Maximum14.700938
Range10.345104
Interquartile range (IQR)2.4905118

Descriptive statistics

Standard deviation1.8537383
Coefficient of variation (CV)0.23006586
Kurtosis1.1338208
Mean8.0574246
Median Absolute Deviation (MAD)1.4833378
Skewness0.81151544
Sum1635.6572
Variance3.4363459
MonotonicityNot monotonic
2023-04-26T10:19:26.680960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.4086 19
 
9.4%
6.189868421 17
 
8.4%
9.800625 17
 
8.4%
7.8405 16
 
7.9%
7.35046875 16
 
7.9%
6.918088235 14
 
6.9%
8.400535714 13
 
6.4%
6.357162162 12
 
5.9%
8.110862069 10
 
4.9%
7.127727273 9
 
4.4%
Other values (20) 60
29.6%
ValueCountFrequency (%)
4.355833333 1
 
0.5%
4.438018868 1
 
0.5%
4.7043 1
 
0.5%
5.004574468 2
 
1.0%
5.113369565 2
 
1.0%
5.470116279 4
 
2.0%
5.600357143 3
 
1.5%
5.73695122 3
 
1.5%
6.031153846 2
 
1.0%
6.189868421 17
8.4%
ValueCountFrequency (%)
14.7009375 2
 
1.0%
13.83617647 1
 
0.5%
13.0675 2
 
1.0%
12.37973684 2
 
1.0%
11.76075 2
 
1.0%
10.69159091 8
3.9%
10.22673913 7
 
3.4%
9.800625 17
8.4%
9.4086 19
9.4%
9.046730769 3
 
1.5%

prix
Real number (ℝ)

Distinct187
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13347.2
Minimum5151
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-26T10:19:26.838387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5151
5-th percentile6229
Q17847
median10345
Q316509
95-th percentile32500.2
Maximum45400
Range40249
Interquartile range (IQR)8662

Descriptive statistics

Standard deviation7995.7399
Coefficient of variation (CV)0.59905745
Kurtosis3.0206407
Mean13347.2
Median Absolute Deviation (MAD)3300
Skewness1.7716934
Sum2709481.7
Variance63931856
MonotonicityNot monotonic
2023-04-26T10:19:26.998538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
18150 2
 
1.0%
7898 2
 
1.0%
8916.5 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (177) 183
90.1%
ValueCountFrequency (%)
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
6229 2
1.0%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

marque
Categorical

Distinct22
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32 
nissan
18 
mazda
17 
mitsubishi
13 
honda
13 
Other values (17)
110 

Length

Max length10
Median length9
Mean length6.2068966
Min length3

Characters and Unicode

Total characters1260
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romeo
2nd rowalfa-romeo
3rd rowalfa-romeo
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 32
15.8%
nissan 18
 
8.9%
mazda 17
 
8.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
volkswagen 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
dodge 9
 
4.4%
Other values (12) 57
28.1%

Length

2023-04-26T10:19:27.165385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
15.8%
nissan 18
 
8.9%
mazda 17
 
8.4%
mitsubishi 13
 
6.4%
honda 13
 
6.4%
volkswagen 12
 
5.9%
peugeot 11
 
5.4%
volvo 11
 
5.4%
subaru 10
 
4.9%
dodge 9
 
4.4%
Other values (12) 57
28.1%

Most occurring characters

ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
7.9%
s 99
 
7.9%
u 80
 
6.3%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 374
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1257
99.8%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
8.0%
s 99
 
7.9%
u 80
 
6.4%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (14) 371
29.5%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1257
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
8.0%
s 99
 
7.9%
u 80
 
6.4%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (14) 371
29.5%
Common
ValueCountFrequency (%)
- 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 152
12.1%
o 152
12.1%
t 100
 
7.9%
s 99
 
7.9%
u 80
 
6.3%
i 76
 
6.0%
n 63
 
5.0%
e 60
 
4.8%
d 55
 
4.4%
m 49
 
3.9%
Other values (15) 374
29.7%

modele
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct140
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
504
 
6
corolla
 
6
corona
 
6
dl
 
4
civic
 
3
Other values (135)
178 

Length

Max length22
Median length16
Mean length6.6748768
Min length2

Characters and Unicode

Total characters1355
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)49.8%

Sample

1st rowgiulia
2nd rowstelvio
3rd rowQuadrifoglio
4th row100ls
5th row100ls

Common Values

ValueCountFrequency (%)
504 6
 
3.0%
corolla 6
 
3.0%
corona 6
 
3.0%
dl 4
 
2.0%
civic 3
 
1.5%
markii 3
 
1.5%
rabbit 3
 
1.5%
g4 3
 
1.5%
outlander 3
 
1.5%
mirageg4 3
 
1.5%
Other values (130) 163
80.3%

Length

2023-04-26T10:19:27.322662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
504 6
 
3.0%
corona 6
 
3.0%
corolla 6
 
3.0%
dl 4
 
2.0%
mirageg4 3
 
1.5%
626 3
 
1.5%
glcdeluxe 3
 
1.5%
dasher 3
 
1.5%
100ls 3
 
1.5%
outlander 3
 
1.5%
Other values (130) 163
80.3%

Most occurring characters

ValueCountFrequency (%)
c 108
 
8.0%
a 107
 
7.9%
l 103
 
7.6%
r 100
 
7.4%
e 100
 
7.4%
o 93
 
6.9%
i 71
 
5.2%
t 67
 
4.9%
s 54
 
4.0%
0 44
 
3.2%
Other values (34) 508
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
83.2%
Decimal Number 179
 
13.2%
Close Punctuation 13
 
1.0%
Open Punctuation 13
 
1.0%
Uppercase Letter 12
 
0.9%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 108
 
9.6%
a 107
 
9.5%
l 103
 
9.1%
r 100
 
8.9%
e 100
 
8.9%
o 93
 
8.2%
i 71
 
6.3%
t 67
 
5.9%
s 54
 
4.8%
u 41
 
3.6%
Other values (15) 284
25.2%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
V 1
 
8.3%
C 1
 
8.3%
Q 1
 
8.3%
U 1
 
8.3%
X 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
84.1%
Common 215
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 108
 
9.5%
a 107
 
9.4%
l 103
 
9.0%
r 100
 
8.8%
e 100
 
8.8%
o 93
 
8.2%
i 71
 
6.2%
t 67
 
5.9%
s 54
 
4.7%
u 41
 
3.6%
Other values (22) 296
26.0%
Common
ValueCountFrequency (%)
0 44
20.5%
4 37
17.2%
1 23
10.7%
2 21
9.8%
5 18
8.4%
) 13
 
6.0%
( 13
 
6.0%
9 12
 
5.6%
6 12
 
5.6%
3 10
 
4.7%
Other values (2) 12
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 108
 
8.0%
a 107
 
7.9%
l 103
 
7.6%
r 100
 
7.4%
e 100
 
7.4%
o 93
 
6.9%
i 71
 
5.2%
t 67
 
4.9%
s 54
 
4.0%
0 44
 
3.2%
Other values (34) 508
37.5%

Interactions

2023-04-26T10:19:16.480935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:45.721594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-26T10:19:00.249542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-26T10:19:12.173213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-26T10:18:58.396355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-26T10:19:14.442000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:16.861003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:46.171820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:48.582361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:50.597770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:52.615839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:54.672410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:56.634849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-26T10:18:57.756166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:59.713118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:01.995549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:03.994132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:05.912543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:07.934355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:09.825759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:11.700077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:13.689737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:16.000805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:18.158272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:47.738508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:49.798650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:51.874630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:53.904451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:55.883576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:57.876697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:59.830424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:02.102542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:04.121621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:06.033448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:08.048689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:09.931339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:11.805388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:13.801594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:16.109177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:18.293281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:47.882713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:49.934148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:51.997170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:54.037969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:56.009397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:58.001391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:59.955706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:02.227191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:04.250588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:06.157598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:08.171345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:10.045270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:11.928624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:13.929212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:16.232560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:18.420483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:48.048647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:50.068531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:52.113759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:54.165296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:56.130802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:18:58.124295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:00.075788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:02.344003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:04.377319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:06.275113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:08.294041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:10.153884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:12.048296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:14.045209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-26T10:19:16.343578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-26T10:19:27.492919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
car_IDniveau_risque_assuranceempattement(cm)longueur_voiture(cm)largeur_voiture(cm)hauteur_voiture(cm)poids_vehicule(kg)taille_moteurtaux_alésage(cm)course_piston(cm)taux_compressionchevauxtour_moteurconsommation_ville(L/100km)consommation_autoroute(L/100km)prixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteurnombre_cylindressysteme_carburantmarque
car_ID1.000-0.1570.2010.1630.1570.2670.1310.0940.263-0.1630.1490.012-0.224-0.047-0.0160.0300.2870.2600.3460.1790.4240.3320.4060.2790.3860.810
niveau_risque_assurance-0.1571.000-0.537-0.394-0.250-0.527-0.258-0.175-0.175-0.0220.029-0.0060.2830.019-0.053-0.1430.2190.1810.6820.3340.2660.2710.2210.1600.2690.442
empattement(cm)0.201-0.5371.0000.9120.8110.6360.7660.6460.5510.218-0.1310.501-0.3170.4930.5380.6820.3420.3080.4370.3330.3920.5680.3440.3160.2250.501
longueur_voiture(cm)0.163-0.3940.9121.0000.8870.5290.8910.7800.6590.176-0.1940.657-0.2770.6690.6970.8040.1040.2060.3620.2390.4090.0000.3150.3560.3260.499
largeur_voiture(cm)0.157-0.2500.8110.8871.0000.3530.8650.7700.6350.228-0.1490.686-0.2070.6880.7010.8110.2320.3220.2320.1160.4070.1590.3720.5680.2340.520
hauteur_voiture(cm)0.267-0.5270.6360.5290.3531.0000.3460.1980.219-0.0320.0040.008-0.3000.0640.1290.2410.3110.2830.5670.4880.3440.1970.3480.2990.2860.485
poids_vehicule(kg)0.131-0.2580.7660.8910.8650.3461.0000.8770.7200.150-0.2160.806-0.2450.8110.8320.9090.3030.3740.2760.2300.4540.0970.3250.4820.2900.492
taille_moteur0.094-0.1750.6460.7800.7700.1980.8771.0000.7150.287-0.2330.815-0.2810.7290.7190.8260.1550.2680.2080.2010.4670.6180.5270.6420.3310.531
taux_alésage(cm)0.263-0.1750.5510.6590.6350.2190.7200.7151.000-0.055-0.1730.662-0.2840.6380.6360.6750.2560.3930.1380.1720.4650.3540.3850.3230.2820.512
course_piston(cm)-0.163-0.0220.2180.1760.228-0.0320.1500.287-0.0551.000-0.0790.121-0.0890.0150.0180.0880.3800.2710.1200.1650.3560.6180.4040.2480.3080.623
taux_compression0.1490.029-0.131-0.194-0.1490.004-0.216-0.233-0.173-0.0791.000-0.353-0.015-0.477-0.443-0.1720.9930.5530.1870.0470.1110.0000.3360.5210.5180.491
chevaux0.012-0.0060.5010.6570.6860.0080.8060.8150.6620.121-0.3531.0000.1070.9110.8840.8540.2210.3410.1640.1880.4000.8430.5180.5640.3170.458
tour_moteur-0.2240.283-0.317-0.277-0.207-0.300-0.245-0.281-0.284-0.089-0.0150.1071.0000.1220.050-0.0770.5940.3100.2390.0710.2460.4470.3620.2820.3630.472
consommation_ville(L/100km)-0.0470.0190.4930.6690.6880.0640.8110.7290.6380.015-0.4770.9110.1221.0000.9680.8280.3120.1860.1190.0750.3860.3630.3380.4970.3160.403
consommation_autoroute(L/100km)-0.016-0.0530.5380.6970.7010.1290.8320.7190.6360.018-0.4430.8840.0500.9681.0000.8220.3610.2980.1700.1790.4340.2310.3450.5140.3360.380
prix0.030-0.1430.6820.8040.8110.2410.9090.8260.6750.088-0.1720.854-0.0770.8280.8221.0000.3290.4040.0000.2290.4440.4500.2850.4300.2870.377
carburant0.2870.2190.3420.1040.2320.3110.3030.1550.2560.3800.9930.2210.5940.3120.3610.3291.0000.3730.1610.1740.0850.0000.2470.1540.9850.368
turbo0.2600.1810.3080.2060.3220.2830.3740.2680.3930.2710.5530.3410.3100.1860.2980.4040.3731.0000.0000.0000.1140.0000.1460.1960.6090.410
nombre_portes0.3460.6820.4370.3620.2320.5670.2760.2080.1380.1200.1870.1640.2390.1190.1700.0000.1610.0001.0000.7390.0510.0680.2020.1350.2460.305
type_vehicule0.1790.3340.3330.2390.1160.4880.2300.2010.1720.1650.0470.1880.0710.0750.1790.2290.1740.0000.7391.0000.2120.4380.1450.0670.1440.321
roues_motrices0.4240.2660.3920.4090.4070.3440.4540.4670.4650.3560.1110.4000.2460.3860.4340.4440.0850.1140.0510.2121.0000.1230.4420.3340.3850.621
emplacement_moteur0.3320.2710.5680.0000.1590.1970.0970.6180.3540.6180.0000.8430.4470.3630.2310.4500.0000.0000.0680.4380.1231.0000.4360.2870.0000.702
type_moteur0.4060.2210.3440.3150.3720.3480.3250.5270.3850.4040.3360.5180.3620.3380.3450.2850.2470.1460.2020.1450.4420.4361.0000.5460.3750.626
nombre_cylindres0.2790.1600.3160.3560.5680.2990.4820.6420.3230.2480.5210.5640.2820.4970.5140.4300.1540.1960.1350.0670.3340.2870.5461.0000.3730.543
systeme_carburant0.3860.2690.2250.3260.2340.2860.2900.3310.2820.3080.5180.3170.3630.3160.3360.2870.9850.6090.2460.1440.3850.0000.3750.3731.0000.508
marque0.8100.4420.5010.4990.5200.4850.4920.5310.5120.6230.4910.4580.4720.4030.3800.3770.3680.4100.3050.3210.6210.7020.6260.5430.5081.000

Missing values

2023-04-26T10:19:18.669030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-26T10:19:19.156505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

car_IDniveau_risque_assurancecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattement(cm)longueur_voiture(cm)largeur_voiture(cm)hauteur_voiture(cm)poids_vehicule(kg)type_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésage(cm)course_piston(cm)taux_compressionchevauxtour_moteurconsommation_ville(L/100km)consommation_autoroute(L/100km)prixmarquemodele
013gasstdtwodécapotablepropulsionfront225.0428.8162.8124.01155.8dohcfour130mpfi8.86.89.0111500011.2007148.71166713495.000alfa-romeogiulia
123gasstdtwodécapotablepropulsionfront225.0428.8162.8124.01155.8dohcfour130mpfi8.86.89.0111500011.2007148.71166716500.000alfa-romeostelvio
231gasstdtwohayonpropulsionfront240.0434.8166.4133.11280.5ohcvsix152mpfi6.88.89.0154500012.3797379.04673116500.000alfa-romeoQuadrifoglio
342gasstdfourberlinetractionfront253.5448.6168.1137.91060.0ohcfour109mpfi8.18.610.010255009.8006257.84050013950.000audi100ls
452gasstdfourberlinequatre_roues_motricesfront252.5448.6168.7137.91280.9ohcfive136mpfi8.18.68.0115550013.06750010.69159117450.000audi100ls
562gasstdtwoberlinetractionfront253.5450.3168.4134.91137.2ohcfive136mpfi8.18.68.5110550012.3797379.40860015250.000audifox
671gasstdfourberlinetractionfront268.7489.5181.4141.51290.0ohcfive136mpfi8.18.68.5110550012.3797379.40860017710.000audi100ls
781gasstdfourbreaktractionfront268.7489.5181.4141.51339.9ohcfive136mpfi8.18.68.5110550012.3797379.40860018920.000audi5000
891gasturbofourberlinetractionfront268.7489.5181.4142.01399.8ohcfive131mpfi8.08.68.3140550013.83617611.76075023875.000audi4000
9100gasturbotwohayonquatre_roues_motricesfront252.7452.6172.5132.11384.8ohcfive131mpfi8.08.67.0160550014.70093810.69159117859.167audi5000s(diesel)
car_IDniveau_risque_assurancecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattement(cm)longueur_voiture(cm)largeur_voiture(cm)hauteur_voiture(cm)poids_vehicule(kg)type_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésage(cm)course_piston(cm)taux_compressionchevauxtour_moteurconsommation_ville(L/100km)consommation_autoroute(L/100km)prixmarquemodele
193196-1gasstdfourbreakpropulsionfront264.9479.6170.7146.01376.2ohcfour141mpfi9.68.09.5114540010.2267398.40053613415.0volvo144ea
194197-2gasstdfourberlinepropulsionfront264.9479.6170.7142.71331.3ohcfour141mpfi9.68.09.511454009.8006258.40053615985.0volvo244dl
195198-1gasstdfourbreakpropulsionfront264.9479.6170.7146.01379.8ohcfour141mpfi9.68.09.511454009.8006258.40053616515.0volvo245
196199-2gasturbofourberlinepropulsionfront264.9479.6170.7142.71381.2ohcfour130mpfi9.28.07.5162510013.83617610.69159118420.0volvo264gl
197200-1gasturbofourbreakpropulsionfront264.9479.6170.7146.01432.0ohcfour130mpfi9.28.07.5162510013.83617610.69159118950.0volvodiesel
198201-1gasstdfourberlinepropulsionfront277.1479.6175.0141.01339.0ohcfour141mpfi9.68.09.5114540010.2267398.40053616845.0volvo145e(sw)
199202-1gasturbofourberlinepropulsionfront277.1479.6174.8141.01383.0ohcfour141mpfi9.68.08.7160530012.3797379.40860019045.0volvo144ea
200203-1gasstdfourberlinepropulsionfront277.1479.6175.0141.01366.2ohcvsix173mpfi9.17.38.8134550013.06750010.22673921485.0volvo244dl
201204-1dieselturbofourberlinepropulsionfront277.1479.6175.0141.01459.2ohcsix145idi7.68.623.010648009.0467318.71166722470.0volvo246
202205-1gasturbofourberlinepropulsionfront277.1479.6175.0141.01388.9ohcfour141mpfi9.68.09.5114540012.3797379.40860022625.0volvo264gl